knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, 
                      tidy = TRUE, message = FALSE, 
                      warning = FALSE)
library(flowCore)
library(flowTime)
library(ggplot2)
library(stringr)                        
library(ggridges)
library(dplyr)
library(tidyr)
library(tidyverse)
library(drc)
library(gridExtra)
library(openCyto)
library(ggcyto)
library(flowStats)
library(flowClust)
library(wesanderson)
library(patchwork)
library(ggthemes)
library(agricolae)

Time-course response and ratiometric measurement of the single-fusion biosensors

Time-course degradation

# Read in flow sets from 20200611 and 20200614
flowSet <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Data/Mahbub/FlowSets/", pattern = "202006", phenoData = "annotation.txt")
flowSet <- flowSet[which(flowSet@phenoData@data$strain %in% c("T1T1", "A2A2"))]

write.flowSet(flowSet, "flowSets/single-time-course")
flowSet <- read.flowSet(path =  "flowSets/single-time-course", phenoData = "annotation.txt")
# load gates for this strain/cytometer
load("PSB_Accuri_W303.RData")
data_sum <- summarizeFlow(flowset = flowSet, ploidy = "diploid", only = "singlets")
[1] "Gating with diploid singlet gates..."
time0_14 <- data_sum %>% dplyr::filter(name == "21D10.fcs") %>% 
  pull(btime) 
time0_11 <- data_sum %>% dplyr::filter(name == "11G02.fcs") %>% 
  pull(btime) 
data_sum <- 
  data_sum %>% 
    mutate(time = case_when(
      folder == "20200611_AFB_epistasis" ~ 
        .$btime - time0_11, 
      folder == "20200614_AFB_epistasis" ~
        .$btime - time0_14
    ))
shapes <- c("DMSO" = 1, "IAA" = 16)
lines <- c("DMSO" = 2, "IAA" = 1)

data_sum$ratio <- data_sum$FL1.Amean/data_sum$FL4.Amean
data_sum$Venus <- data_sum$FL1.Amean/10000
data_sum_long <- data_sum %>% 
  dplyr::select(time, treatment, yWL, folder, strain, ratio, Venus) %>%
  pivot_longer(cols = c(ratio, Venus), names_to = "parameter")
deg_plot <- ggplot(data = subset(data_sum_long, yWL == "166"), 
       aes(x = time, y = value, shape = treatment, 
           color = treatment, 
           group = interaction(treatment, folder))) + 
  geom_point() + labs(y = "intensity (AU)", x = "time post auxin addition (min)") +
  facet_wrap(~fct_rev(parameter), scales = "free") + 
  scale_shape_manual(values = shapes) + 
  geom_line(aes(linetype = treatment)) +
  scale_linetype_manual(values = lines) + 
  scale_color_viridis_d(option = "D", end = 0.75, direction = -1) + 
  theme_test()
deg_plot

data <- flowTime::tidyFlow(flowSet, ploidy = "diploid", only = "singlets")
[1] "No further gating applied."
[1] "Converting events..."
# get late time points for one strain
last_reading <- 
  tail(unique(data[which(data$yWL == 166), "name"]),4) %>% head(2)
data <- dplyr::filter(data, yWL == "166" & name %in% last_reading)
# clean this up, cut off zeros
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
[1]     21 697262
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
[1] 0.1842678
range(data$FLratio)
[1] 0.002628614 3.836085188

Single-fusion CV plot

#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
# make a tidy, long dataset 
data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 

# need to also format CVs approriately for annotating
cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + coord_cartesian(x = c(0,2)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(fct_relevel(parameter, "Venus")~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "IAA"), 
             aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CVs, treatment == "DMSO"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 2) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot

layout <- "
AAAAB
CCCCC
"
CV_plot  + guide_area() + deg_plot + plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(5, 2), design = layout) 
ggsave("ratio-deg.pdf", width = 5, height = 5)
ggsave("ratio-deg.png", width = 5, height = 5)

Dose-response curves of dual-fusion AFB2 and TIR1 biosensors

AFB2-based biosensor (yWL210 AFB2 dual-fusion, single ratiometric construct)

plate_all_210 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL210/All data/", pattern = "DRA-*") 

annotation <- createAnnotation(yourFlowSet = plate_all_210)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv')
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv'
  )

aplate_all_210 <-
  annotateFlowSet(yourFlowSet = plate_all_210,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_210)))
head(pData(aplate_all_210))
write.flowSet(aplate_all_210, 
              outdir = "flowSets/AFB2-dual-yWL210-dose-response")
aplate_all_210 <-
  read.flowSet(path = "flowSets/AFB2-dual-yWL210-dose-response", 
               phenoData = "annotation.txt")

plate_all_210_sum <-
  summarizeFlow(aplate_all_210, channel = NA, gated = TRUE)
[1] "Summarizing all events..."

###Dose-response curve

Comparing log-logistic and Weibull models

(Figure 2 in Ritz (2009))

fitdrc.m1 <-
  drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = LL.4())
fitdrc.m2 <-
 drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = W1.4())
#fitdrc.m3 <- 
# drm(YL1.Amean/BL1.Amean~dose, data=plate_all_210_sum, fct = W2.4())
#Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, :
#Convergence failed

summary(fitdrc.m1)

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                Estimate Std. Error t-value p-value    
b:(Intercept) -1.5032417  0.6338695 -2.3715 0.01985 *  
c:(Intercept)  0.0526074  0.0020790 25.3043 < 2e-16 ***
d:(Intercept)  0.0845079  0.0021128 39.9976 < 2e-16 ***
e:(Intercept)  0.0403739  0.0159429  2.5324 0.01306 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.01246335 (90 degrees of freedom)
summary(fitdrc.m2)

Model fitted: Weibull (type 1) (4 parms)

Parameter estimates:

                Estimate Std. Error t-value   p-value    
b:(Intercept) -0.7846961  0.2493021 -3.1476  0.002233 ** 
c:(Intercept)  0.0522487  0.0021769 24.0009 < 2.2e-16 ***
d:(Intercept)  0.0847793  0.0022414 37.8241 < 2.2e-16 ***
e:(Intercept)  0.0189778  0.0111048  1.7090  0.090902 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.01263587 (90 degrees of freedom)
#summary(fitdrc.m3)

The 4-parameter log-logistic model has a slightly lower residual standard error.

model.LL4_all_210 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = plate_all_210_sum,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)

summary(model.LL4_all_210)

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                          Estimate Std. Error t-value p-value    
Slope:(Intercept)       -1.5032417  0.6338695 -2.3715 0.01985 *  
Lower Limit:(Intercept)  0.0526074  0.0020790 25.3043 < 2e-16 ***
Upper Limit:(Intercept)  0.0845079  0.0021128 39.9976 < 2e-16 ***
ED50:(Intercept)         0.0403739  0.0159429  2.5324 0.01306 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.01246335 (90 degrees of freedom)

Individual replicate dose-response curves

replicate1_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "1"),
    fct = LL.4()
  )
replicate2_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "2"),
    fct = LL.4()
  )
replicate3_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "3"),
    fct = LL.4()
  )
replicate4_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "4"),
    fct = LL.4()
  )
replicate5_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "5"),
    fct = LL.4()
  )
replicate6_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "6"),
    fct = LL.4()
  )
replicate7_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "7"),
    fct = LL.4()
  )

plot(
  replicate1_210,
  broken = TRUE,
  type = "all",
  col = "dark green",
  lty = 2
)
plot(
  replicate2_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark blue",
  lty = 2
)
plot(
  replicate3_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "yellow",
  lty = 2
)
plot(
  replicate4_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark grey",
  lty = 2
)
plot(
  replicate5_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark orange",
  lty = 2
)
plot(
  replicate6_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "brown",
  lty = 2
)
plot(
  replicate7_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark red",
  lty = 2
)
plot(
  model.LL4_all_210,
  broken = TRUE,
  add = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)

TIR1-based biosensor (yWL209 TIR dual-fusion, single ratiometric construct)

plate_all_209 <-
  read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL209/", pattern = "DRA-*") 
annotation <- createAnnotation(yourFlowSet = plate_all_209)
write.csv(
  annotation,
  '/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
)
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
  )

aplate_all_209 <-
  annotateFlowSet(yourFlowSet = plate_all_209,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_209)))
head(pData(aplate_all_209))

write.flowSet(aplate_all_209, outdir = "flowSets/TIR1-dual-yWL209-dose-response")
aplate_all_209 <-
  read.flowSet(path = "flowSets/TIR1-dual-yWL209-dose-response", 
               phenoData = "annotation.txt")

dat_sum_overlaydata_209 <-
  summarizeFlow(aplate_all_209, gated = TRUE)
[1] "Summarizing all events..."

###Dose-response curve

model.LL4_rep1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep1_209,
  type = "all",
  col = "red",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep1_209,
  broken = TRUE,
  type = "confidence",
  col = "red",
  add = TRUE
)

print(summary(model.LL4_rep1_209))

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                          Estimate Std. Error t-value   p-value    
Slope:(Intercept)       -0.3671010  0.1395354 -2.6309   0.03901 *  
Lower Limit:(Intercept)  0.0960915  0.0019149 50.1808 4.201e-09 ***
Upper Limit:(Intercept)  0.1200258  0.0068403 17.5470 2.198e-06 ***
ED50:(Intercept)         1.4438822  2.8035445  0.5150   0.62496    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.002705295 (6 degrees of freedom)
model.LL4_rep2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep2_209,
  type = "all",
  col = "blue",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep2_209,
  broken = TRUE,
  type = "confidence",
  col = "blue",
  add = TRUE
)

print(summary(model.LL4_rep2_209))

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                          Estimate Std. Error t-value   p-value    
Slope:(Intercept)       -0.4323121  0.1153679 -3.7472  0.005646 ** 
Lower Limit:(Intercept)  0.0998826  0.0018914 52.8084 1.833e-11 ***
Upper Limit:(Intercept)  0.1372100  0.0062312 22.0197 1.910e-08 ***
ED50:(Intercept)         1.4546642  1.4213107  1.0235  0.336036    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.003755989 (8 degrees of freedom)
model.LL4_rep3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep3_209,
  type = "all",
  col = "dark green",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep3_209,
  broken = TRUE,
  type = "confidence",
  col = "dark green",
  add = TRUE
)


print(summary(model.LL4_rep3_209))

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                          Estimate Std. Error t-value   p-value    
Slope:(Intercept)       -0.4007190  0.2177393 -1.8404    0.1030    
Lower Limit:(Intercept)  0.0984103  0.0018186 54.1128 1.509e-11 ***
Upper Limit:(Intercept)  0.1148849  0.0037802 30.3916 1.492e-09 ***
ED50:(Intercept)         0.9199369  1.1883595  0.7741    0.4611    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.002908608 (8 degrees of freedom)
model.LL4_all_209 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = dat_sum_overlaydata_209,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_209,
  type = "all",
  col = "black",
  lty = 1,
  lwd = 3
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  col = "black",
  add = TRUE
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)
print(summary(model.LL4_all_209))

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                          Estimate Std. Error t-value   p-value    
Slope:(Intercept)       -0.3420811  0.0895742 -3.8190 0.0004353 ***
Lower Limit:(Intercept)  0.0973731  0.0014017 69.4690 < 2.2e-16 ***
Upper Limit:(Intercept)  0.1337956  0.0109331 12.2377 1.972e-15 ***
ED50:(Intercept)        10.9140480 20.3972838  0.5351 0.5954208    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 0.004904408 (42 degrees of freedom)
replicate1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4()
  )
replicate2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4()
  )
replicate3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4()
  )


plot(
  replicate1_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "red",
  lty = 2
)
plot(
  replicate2_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "blue",
  lty = 2
)
plot(
  replicate3_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "dark green",
  lty = 2
)

Combined Dose-response curveswith ggplot

pm210 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm210 <-
  cbind(pm210,
        predict(model.LL4_all_210, newdata = pm210, interval = "confidence"))  

plot210 <-
  ggplot(plate_all_210_sum, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm210,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm210,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + ylab("mScarlet-I/Venus") + xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) +  
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) + 
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  ggtitle("AFB2")  
plot210



pm209 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm209 <-
  cbind(pm209,
        predict(model.LL4_all_209, newdata = pm209, interval = "confidence"))

plot209 <-
  ggplot(dat_sum_overlaydata_209, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm209,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm209,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + 
  ylab("mScarlet-I/Venus") + 
  xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) +
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  #ylim(0.095, 0.12) + 
  ggtitle("TIR1")
plot209


plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A")

ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.pdf",
  width = 6,
  height = 3
)
ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.png",
  width = 6,
  height = 3
)

LCMS measurements of intracellular auxin

plate_08052022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/Alldata/", pattern = "DRA*") 
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/08052022_alldata_annotation.csv')

aplate_08052022 <- annotateFlowSet(yourFlowSet = plate_08052022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_08052022)))
head(pData(aplate_08052022))
write.flowSet(aplate_08052022, "flowSets/AFB2-dual-DR-LCMS")
summary(model.LL4_08052022_LCMS)

Model fitted: Log-logistic (ED50 as parameter) (4 parms)

Parameter estimates:

                           Estimate  Std. Error t-value   p-value    
Slope:(Intercept)       -5.6514e-01  8.9868e-02 -6.2885 4.016e-05 ***
Lower Limit:(Intercept)  3.7324e-02  3.6766e-01  0.1015    0.9208    
Upper Limit:(Intercept)  2.6037e+02  2.7474e+02  0.9477    0.3620    
ED50:(Intercept)         1.1866e+05  2.1885e+05  0.5422    0.5976    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error:

 1.019652 (12 degrees of freedom)

Auxin-induced degradation time-course assay for the dual-fusion biosensors in different yeast strains

Two isolated colonies from each strain were selected at random and tested in auxin-induced protein degradation assay. IAA working solution was added to obtain the IAA concentration at 50 µM in each culture. The assay was carried out using ThermoFisher Attune NxT B/Y flow cytometer.

Strains:

plate_03112022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/10readings/onlyPatstrains/", pattern = "TCA*") 
annotation <- createAnnotation(yourFlowSet = plate_03112022)
write.csv(annotation,'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
aplate_03112022 <- annotateFlowSet(yourFlowSet = plate_03112022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_03112022)))
head(pData(aplate_03112022))

write.flowSet(aplate_03112022, outdir = "flowSets/dual-time-course")
aplate_03112022 <-
  read.flowSet(path = "flowSets/dual-time-course/", phenoData = "annotation.txt")
plate_03112022_sum <- summarizeFlow(aplate_03112022, gated = TRUE)
[1] "Summarizing all events..."
plate_03112022_sum <-
  plate_03112022_sum %>% mutate(
    background_p = case_when(
      strain %in% c("yWL185", "yWL186") ~ "W303",
      strain %in% c("yWL209", "yWL210") ~ "YPH499"
    ),
    receptor_p = case_when(
      strain %in% c("yWL185", "yWL209") ~ "TIR1",
      strain %in% c("yWL186", "yWL210") ~ "AFB2"
    )
  )
#The time auxin addition is equal to time zero
time0 <-
  "303112022-Pat-TCA03_Time-course assay_Auxin_yWL185-C1.fcs"
# or whatever well was being read when auxin was added

plate_03112022_sum$time <-
  plate_03112022_sum$btime - 
  plate_03112022_sum[[which(plate_03112022_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"
#To normalize data

plate_03112022_sum <-
  plate_03112022_sum %>% mutate(ratio = BL1.Amean/YL1.Amean) %>%
  group_by(background_p, receptor_p) %>% 
  mutate(normalizedratio = ratio/mean(ratio))
ratio <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = normalizedratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Normalized Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p) +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio

Without normalization

ratio_raw <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = ratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p, scales = "free") +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio_raw

ratio_raw / ratio + plot_annotation(tag_levels = "A") & theme(plot.background = element_blank())
ggsave("tc-strains.pdf", width = 5, height = 7)
ggsave("tc-strains.png", width = 5, height = 7)

CV plot

Using the above time course dataset we can compare coefficients of variation in the individual parameters and the ratio for each of the strains and biosensors at steady state.

yWL185 (TIR1 dual-fusion, W303 yeast)


data185 <- steadyState(aplate_03112022, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
data185 <- subset(data185, strain == "yWL185" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL185-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL185-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data185 <- subset(data185, BL1.A >1 & YL1.A > 1)
data185$FLratio <- data185$BL1.A/data185$YL1.A
range(data185$BL1.A)
[1]      45 1048575
cv <- function(x) return(round(sd(x)/mean(x),2))

data185$Venus <- data185$BL1.A / median(data185$BL1.A)
data185$mScarlet <- data185$YL1.A / median(data185$YL1.A)
data185$FLratio <- data185$BL1.A / data185$YL1.A
data185$ratio <- data185$FLratio / median(data185$FLratio)



data_long185 <- data185 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV185 <-
  data185 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data185

plot185 <- ggplot(data = data_long185, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV185, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV185, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus185 <- ggplot(data185, aes(x=data185$Venus, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "Venus", y ="Density") + xlim(-0.1,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
#venus185

red185 <- ggplot(data185, aes(x=data185$mScarlet, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "mScarlet", y ="Density") + xlim(-0.1,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
#red185


ratio185<- ggplot(data185, aes(x=data185$ratio, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(-0.1,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV185, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.4, y = 2) + 
  geom_text(data = subset(CV185, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

#ratio185

plot185 <- grid.arrange(venus185, red185, ratio185, nrow=3, ncol=1)

plot185
TableGrob (3 x 1) "arrange": 3 grobs

yWL186 (AFB2 dual-fusion, W303 yeast)

data186 <- steadyState(aplate_03112022, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
data186 <- subset(data186, strain == "yWL186" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL186-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL186-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data186 <- subset(data186, BL1.A >1 & YL1.A > 1)
data186$FLratio <- data186$BL1.A/data186$YL1.A
range(data186$BL1.A)
[1]      25 1048575
cv <- function(x) return(round(sd(x)/mean(x),2))

data186$Venus <- data186$BL1.A / median(data186$BL1.A)
data186$mScarlet <- data186$YL1.A / median(data186$YL1.A)
data186$FLratio <- data186$BL1.A / data186$YL1.A
data186$ratio <- data186$FLratio / median(data186$FLratio)



data_long186 <- data186 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV186 <-
  data186 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data186

plot186 <- ggplot(data = data_long186, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV186, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV186, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 



venus186 <- ggplot(data186, aes(x=data186$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus (normalized median)", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV186, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red186 <- ggplot(data186, aes(x=data186$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet (normalized median)", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") +  scale_fill_manual(values=c("#EC7063", "#999999")) + geom_text(data = subset(CV186, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio186<- ggplot(data186, aes(x=data186$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV186, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 2) + 
  geom_text(data = subset(CV186, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.3, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

plot186 <- grid.arrange(venus186, red186, ratio186, nrow=3, ncol=1)

plot186
TableGrob (3 x 1) "arrange": 3 grobs

yWL209 (TIR1 dual-fusion, YPH499 yeast)


data209 <- steadyState(aplate_03112022, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
data209 <- subset(data209, strain == "yWL209" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL209-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL209-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data209 <- subset(data209, BL1.A >1 & YL1.A > 1)
data209$FLratio <- data209$BL1.A/data209$YL1.A
range(data209$BL1.A)
[1]       9 1048575
cv <- function(x) return(round(sd(x)/mean(x),2))

data209$Venus <- data209$BL1.A / median(data209$BL1.A)
data209$mScarlet <- data209$YL1.A / median(data209$YL1.A)
data209$FLratio <- data209$BL1.A / data209$YL1.A
data209$ratio <- data209$FLratio / median(data209$FLratio)



data_long209 <- data209 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV209 <-
  data209 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data209

plot209 <- ggplot(data = data_long209, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV209, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1) + 
  geom_text(data = subset(CV209, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus209 <- ggplot(data209, aes(x=data209$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red209 <- ggplot(data209, aes(x=data209$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio209<- ggplot(data209, aes(x=data209$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV209, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CV209, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))


plot209 <- grid.arrange(venus209, red209, ratio209, nrow=3, ncol=1)

plot209
TableGrob (3 x 1) "arrange": 3 grobs

yWL210 (AFB2 dual-fusion, YPH499 yeast)


data210 <- steadyState(aplate_03112022, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
#data <- tidyFlow(aplate_20210619_W303)

data210 <- subset(data210, strain == "yWL210" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL210-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL210-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data210 <- subset(data210, BL1.A >1 & YL1.A > 1)
data210$FLratio <- data210$BL1.A/data210$YL1.A
range(data210$BL1.A)
[1]      25 1048575
cv <- function(x) return(round(sd(x)/mean(x),2))

data210$Venus <- data210$BL1.A / median(data210$BL1.A)
data210$mScarlet <- data210$YL1.A / median(data210$YL1.A)
data210$FLratio <- data210$BL1.A / data210$YL1.A
data210$ratio <- data210$FLratio / median(data210$FLratio)



data_long210 <- data210 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV210 <-
  data210 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data210

plot210 <- ggplot(data = data_long210, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV210, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV210, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) +
  facet_grid(parameter~.) 




venus210 <- ggplot(data210, aes(x=data210$Venus, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") +  geom_text(data = subset(CV210, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.5, y = 1) + 
  geom_text(data = subset(CV210, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.7, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red210 <- ggplot(data210, aes(x=data210$mScarlet, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.2) + 
  geom_text(data = subset(CV210, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.4, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio210<- ggplot(data210, aes(x=data210$ratio, group=treatment, color=treatment, fill = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV210, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 1.8) + 
  geom_text(data = subset(CV210, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 1.8) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
#ratio210

plot210 <- grid.arrange(venus210, red210, ratio210, nrow=3, ncol=1)

plot210
TableGrob (3 x 1) "arrange": 3 grobs
dual_fusion_cv <- (venus210 + ggtitle("AFB2")+ 
                theme(plot.title = element_text(hjust = 0.5)) | 
                venus209 + ggtitle("TIR1") + 
                theme(plot.title = element_text(hjust = 0.5))) / 
               (red210 | red209) / 
             (ratio210 | ratio209) + 
  theme(title = element_text(hjust = 0))
dual_fusion_cv 
ggsave("dual_fusion_cv.pdf", width = 6.3, height = 6)
ggsave("dual_fusion_cv.png", width = 6.3, height = 6)

Single-fusion vs dual-fusion comparison in W303 yeast strain

plate_20210619_W303 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only/", pattern = "r*") 
annotation <- createAnnotation(yourFlowSet = plate_20210619_W303)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
aplate_20210619_W303 <- annotateFlowSet(yourFlowSet = plate_20210619_W303, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_W303)))
head(pData(aplate_20210619_W303))
write.flowSet(aplate_20210619_W303, outdir = "flowSets/design-relative-expression")
aplate_20210619_W303 <- read.flowSet(path = "flowSets/design-relative-expression/", phenoData = "annotation.txt")
plate_20210619_W303_sum <- summarizeFlow(aplate_20210619_W303,  gated = TRUE)
[1] "Summarizing all events..."
#The time auxin addition is equal to time zero
time0 <- "4E01.fcs" 
# or whatever well was being read when auxin was added

plate_20210619_W303_sum$time <- plate_20210619_W303_sum$btime-plate_20210619_W303_sum[[which(plate_20210619_W303_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"
plate_20210619_W303_sum <- plate_20210619_W303_sum %>%
  mutate(
    design = case_when(
      strain %in% c("yWL185","yWL186") ~ "dual-fusion",
    strain %in% c("yWL161","yWL162") ~ "single-fusion"), 
         fbox = case_when(
    strain %in% c("yWL161","yWL185") ~ "TIR1",
    strain %in% c("yWL162","yWL186") ~ "AFB2")) %>%
  mutate(design_construct = paste(fbox, design)) 

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(ratio = FL1.Amean/FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalizedratio = ratio/mean(ratio))

plate_20210619_W303_sum<-
  plate_20210619_W303_sum %>% mutate(green = FL1.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Greenexpression = FL1.Amean/mean(FL1.Amean))

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(red = FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Redexpression = FL4.Amean/mean(FL4.Amean))
TIR1_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL161"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 


AFB2_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL162"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

TIR1_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL185"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + xlab("Time (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

AFB2_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL186"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

grid.arrange(AFB2_single, TIR1_single, TIR1_dual, AFB2_dual, nrow=2, ncol=4)

W303ratio <- qplot(x = time, y = normalizedratio, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus") + scale_color_manual(values = c("#2E86C1", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

W303venus <- qplot(x = time, y = normalized_Greenexpression, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet") + scale_color_manual(values = c("#F1C40F", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

grid.arrange(W303venus, W303ratio, nrow=2, ncol=2)

Venus_aov <- aov(FL1.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(Venus_aov)
                                        Df    Sum Sq   Mean Sq F value   Pr(>F)    
design_construct                         3 5.902e+09 1.967e+09 108.760  < 2e-16 ***
treatment                                1 9.695e+08 9.695e+08  53.601 5.82e-09 ***
before_after                             1 4.570e+06 4.570e+06   0.253    0.618    
design_construct:treatment               3 5.293e+07 1.764e+07   0.976    0.414    
design_construct:before_after            3 9.180e+07 3.060e+07   1.692    0.184    
treatment:before_after                   1 6.641e+08 6.641e+08  36.715 3.56e-07 ***
design_construct:treatment:before_after  3 3.063e+07 1.021e+07   0.564    0.642    
Residuals                               41 7.416e+08 1.809e+07                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Venus_HSD.test <- 
    agricolae::HSD.test(Venus_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))

Study: Venus_aov ~ c("design_construct", "treatment", "before_after")

HSD Test for FL1.Amean 

Mean Square Error:  18087298 

design_construct:treatment:before_after,  means

Alpha: 0.05 ; DF Error: 41 
Critical Value of Studentized Range: 5.154955 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.
$statistics
   MSerror Df     Mean       CV
  18087298 41 49475.37 8.596028

$parameters
   test                                  name.t ntr StudentizedRange alpha
  Tukey design_construct:treatment:before_after  16         5.154955  0.05

$means
                                       FL1.Amean      std r      Min      Max      Q25      Q50      Q75
AFB2 dual-fusion:Auxin:after            24425.97 3887.943 4 19046.09 27855.18 22934.16 25401.31 26893.13
AFB2 dual-fusion:Auxin:before           34631.51 2164.181 3 33214.07 37122.60 33385.97 33557.87 35340.23
AFB2 dual-fusion:Control EtOH:after     43943.78 2563.443 4 40314.31 46305.36 43299.85 44577.73 45221.67
AFB2 dual-fusion:Control EtOH:before    35281.28  747.563 3 34785.81 36141.17 34851.33 34916.85 35529.01
AFB2 single-fusion:Auxin:after          48152.32 8233.437 4 41566.06 58870.26 41748.87 46086.48 52489.94
AFB2 single-fusion:Auxin:before         52801.44 3789.368 3 49120.11 56690.32 50856.99 52593.87 54642.10
AFB2 single-fusion:Control EtOH:after   61495.06 1241.776 4 59958.17 62859.15 60845.16 61581.46 62231.36
AFB2 single-fusion:Control EtOH:before  53911.90 2286.620 3 51418.04 55909.91 52912.90 54407.76 55158.83
TIR1 dual-fusion:Auxin:after            41225.93 2818.571 4 37362.85 43733.63 40076.43 41903.61 43053.11
TIR1 dual-fusion:Auxin:before           46003.40 1595.801 3 44645.69 47761.17 45124.52 45603.34 46682.25
TIR1 dual-fusion:Control EtOH:after     53108.52 5964.120 4 46969.08 60966.52 49652.60 52249.25 55705.17
TIR1 dual-fusion:Control EtOH:before    44671.84 1768.004 3 43508.04 46706.33 43654.60 43801.16 45253.75
TIR1 single-fusion:Auxin:after          53550.99 3388.730 4 50475.84 58381.61 51941.54 52673.26 54282.71
TIR1 single-fusion:Auxin:before         63962.75 3201.204 3 61073.09 67403.85 62242.20 63411.32 65407.59
TIR1 single-fusion:Control EtOH:after   65619.10 7089.748 5 53646.79 71126.31 64830.36 68765.73 69726.29
TIR1 single-fusion:Control EtOH:before  64865.95 3237.883 3 61178.65 67245.10 63676.38 66174.10 66709.60

$comparison
NULL

$groups
                                       FL1.Amean groups
TIR1 single-fusion:Control EtOH:after   65619.10      a
TIR1 single-fusion:Control EtOH:before  64865.95     ab
TIR1 single-fusion:Auxin:before         63962.75     ab
AFB2 single-fusion:Control EtOH:after   61495.06     ab
AFB2 single-fusion:Control EtOH:before  53911.90     bc
TIR1 single-fusion:Auxin:after          53550.99     bc
TIR1 dual-fusion:Control EtOH:after     53108.52     bc
AFB2 single-fusion:Auxin:before         52801.44    bcd
AFB2 single-fusion:Auxin:after          48152.32     cd
TIR1 dual-fusion:Auxin:before           46003.40    cde
TIR1 dual-fusion:Control EtOH:before    44671.84    cde
AFB2 dual-fusion:Control EtOH:after     43943.78    cde
TIR1 dual-fusion:Auxin:after            41225.93     de
AFB2 dual-fusion:Control EtOH:before    35281.28     ef
AFB2 dual-fusion:Auxin:before           34631.51     ef
AFB2 dual-fusion:Auxin:after            24425.97      f

attr(,"class")
[1] "group"
Venus_groups <- Venus_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(Venus_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))
mScarlet_aov <- aov(FL4.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(mScarlet_aov)
                                        Df    Sum Sq   Mean Sq  F value   Pr(>F)    
design_construct                         3 1.318e+10 4.393e+09 1152.935  < 2e-16 ***
treatment                                1 2.843e+06 2.843e+06    0.746  0.39279    
before_after                             1 7.962e+07 7.962e+07   20.894 4.41e-05 ***
design_construct:treatment               3 4.175e+06 1.392e+06    0.365  0.77844    
design_construct:before_after            3 6.634e+07 2.211e+07    5.803  0.00211 ** 
treatment:before_after                   1 7.820e+02 7.820e+02    0.000  0.98864    
design_construct:treatment:before_after  3 7.087e+06 2.362e+06    0.620  0.60610    
Residuals                               41 1.562e+08 3.811e+06                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(mScarlet_HSD.test <- 
    agricolae::HSD.test(mScarlet_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))

Study: mScarlet_aov ~ c("design_construct", "treatment", "before_after")

HSD Test for FL4.Amean 

Mean Square Error:  3810672 

design_construct:treatment:before_after,  means

Alpha: 0.05 ; DF Error: 41 
Critical Value of Studentized Range: 5.154955 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.
$statistics
  MSerror Df    Mean       CV
  3810672 41 19226.9 10.15293

$parameters
   test                                  name.t ntr StudentizedRange alpha
  Tukey design_construct:treatment:before_after  16         5.154955  0.05

$means
                                       FL4.Amean        std r       Min       Max       Q25       Q50
AFB2 dual-fusion:Auxin:after            2490.844  198.85812 4  2207.416  2666.530  2438.961  2544.714
AFB2 dual-fusion:Auxin:before           2026.397   46.66275 3  1973.433  2061.453  2008.869  2044.305
AFB2 dual-fusion:Control EtOH:after     2759.117  144.66745 4  2612.374  2929.881  2655.805  2747.106
AFB2 dual-fusion:Control EtOH:before    2110.115  112.55555 3  2013.715  2233.807  2048.269  2082.823
AFB2 single-fusion:Auxin:after         36197.490 5828.74906 4 31851.266 44192.014 31870.387 34373.340
AFB2 single-fusion:Auxin:before        31074.988 1904.65789 3 29016.085 32774.074 30225.445 31434.806
AFB2 single-fusion:Control EtOH:after  38385.588 1153.01803 4 36886.238 39681.451 37980.543 38487.331
AFB2 single-fusion:Control EtOH:before 31251.821  936.56398 3 30193.587 31973.960 30890.752 31587.918
TIR1 dual-fusion:Auxin:after            5753.411  381.70386 4  5202.843  6084.835  5692.279  5862.984
TIR1 dual-fusion:Auxin:before           4582.236  107.55234 3  4483.615  4696.914  4524.898  4566.180
TIR1 dual-fusion:Control EtOH:after     5556.851  452.28202 4  5075.024  6086.477  5250.871  5532.952
TIR1 dual-fusion:Control EtOH:before    4557.292  144.44763 3  4390.506  4642.111  4514.882  4639.258
TIR1 single-fusion:Auxin:after         34660.947 2185.28924 4 33220.282 37917.098 33608.882 33753.203
TIR1 single-fusion:Auxin:before        31775.292 1187.70866 3 30675.467 33034.740 31145.568 31615.670
TIR1 single-fusion:Control EtOH:after  34156.499 2380.95848 5 30640.164 37060.388 33524.915 34184.394
TIR1 single-fusion:Control EtOH:before 33266.440  288.20961 3 32971.733 33547.680 33125.820 33279.906
                                             Q75
AFB2 dual-fusion:Auxin:after            2596.597
AFB2 dual-fusion:Auxin:before           2052.879
AFB2 dual-fusion:Control EtOH:after     2850.417
AFB2 dual-fusion:Control EtOH:before    2158.315
AFB2 single-fusion:Auxin:after         38700.443
AFB2 single-fusion:Auxin:before        32104.440
AFB2 single-fusion:Control EtOH:after  38892.376
AFB2 single-fusion:Control EtOH:before 31780.939
TIR1 dual-fusion:Auxin:after            5924.116
TIR1 dual-fusion:Auxin:before           4631.547
TIR1 dual-fusion:Control EtOH:after     5838.933
TIR1 dual-fusion:Control EtOH:before    4640.685
TIR1 single-fusion:Auxin:after         34805.267
TIR1 single-fusion:Auxin:before        32325.205
TIR1 single-fusion:Control EtOH:after  35372.637
TIR1 single-fusion:Control EtOH:before 33413.793

$comparison
NULL

$groups
                                       FL4.Amean groups
AFB2 single-fusion:Control EtOH:after  38385.588      a
AFB2 single-fusion:Auxin:after         36197.490     ab
TIR1 single-fusion:Auxin:after         34660.947     ab
TIR1 single-fusion:Control EtOH:after  34156.499     ab
TIR1 single-fusion:Control EtOH:before 33266.440     ab
TIR1 single-fusion:Auxin:before        31775.292      b
AFB2 single-fusion:Control EtOH:before 31251.821      b
AFB2 single-fusion:Auxin:before        31074.988      b
TIR1 dual-fusion:Auxin:after            5753.411      c
TIR1 dual-fusion:Control EtOH:after     5556.851      c
TIR1 dual-fusion:Auxin:before           4582.236      c
TIR1 dual-fusion:Control EtOH:before    4557.292      c
AFB2 dual-fusion:Control EtOH:after     2759.117      c
AFB2 dual-fusion:Auxin:after            2490.844      c
AFB2 dual-fusion:Control EtOH:before    2110.115      c
AFB2 dual-fusion:Auxin:before           2026.397      c

attr(,"class")
[1] "group"
mScarlet_groups <- mScarlet_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(mScarlet_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))
boxvenus <- 
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL1.Amean/1000,
         )) +
  geom_boxplot(
    aes(fill = fct_rev(treatment)), alpha = 0.7,
    outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
  geom_text(data = Venus_groups, 
            mapping = aes(y = 1.05*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#edc215")) +
  scale_color_manual(values = c("#8f8f8f", "#edc215")) +
  ylab("Venus") + 
  xlab("50 µM Auxin treatment") +  
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct) + 
  theme_classic() + labs(color = "") 


boxmScarlet <-
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL4.Amean/1000
         )) +
  geom_boxplot(aes(fill = fct_rev(treatment)), alpha = .7,
               outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
   geom_text(data = mScarlet_groups, 
            mapping = aes(y = 1.2*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#EC7063")) +
  scale_color_manual(values = c("#8f8f8f", "#EC7063")) +
  ylab("mScarlet-I") + 
  xlab("50 µM Auxin treatment") +   
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct, scales = "free_y") +
  theme_classic() + labs(color = "") + scale_y_log10()


guide_area() / boxvenus / boxmScarlet + 
  plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(.3, 1,1)) & 
  theme(legend.box = "horizontal", 
        legend.spacing = unit(0, "pt"), 
        legend.justification = "top", 
        legend.margin = margin(), 
        legend.box.spacing = unit(0, "pt"), 
        legend.background = element_blank(), 
        legend.box.background = element_blank(), 
        legend.title = element_blank(), 
        legend.key = element_blank())

ggsave("relative-expression-box.pdf", width = 6, height = 5)
ggsave("relative-expression-box.png", width = 6, height = 5)

plate_20210619_read3and12 <- read.flowSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only_read3and12/", alter.names = TRUE) 
annotation <- createAnnotation(yourFlowSet = plate_20210619_read3and12)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
aplate_20210619_read3and12<- annotateFlowSet(yourFlowSet = plate_20210619_read3and12, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_read3and12)))
[1] "D01.fcs" "D02.fcs" "D03.fcs" "D04.fcs" "D07.fcs" "D08.fcs"
head(pData(aplate_20210619_read3and12))
NA
W303_read3and12 <- summarizeFlow(aplate_20210619_read3and12,  gated = TRUE)
[1] "Summarizing all events..."
W303_read3and12 <- W303_read3and12 %>% 
  mutate(design = str_extract(design_construct, "(?<=\\s).*")) %>%
  mutate(design = str_extract(design_construct, ".*(?=\\s)")) 

venus3and12_unnorm <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL1.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), size = 2,
             position = position_jitterdodge(
               dodge.width = 0.7, 
               jitter.width = 0.5)) + 
  scale_color_manual(values=c("#F1C40F", "#5F6A6A")) +
  ylab("Venus") + theme_classic() +
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

mScarlet3and12_unnorm  <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL4.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), 
             size = 2, 
             position = position_jitterdodge(
               dodge.width = .7, 
               jitter.width =.5)) + 
  scale_color_manual(values=c("#E74C3C", "#5F6A6A")) +
  ylab("mScarlet-I") + theme_classic() + 
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

guide_area() / (venus3and12_unnorm | mScarlet3and12_unnorm) + 
  plot_layout(guides = "collect", 
              heights = c(1, 3))  +
  plot_annotation(tag_levels = "A") &
  theme(legend.position='top', legend.direction = "vertical",
        legend.title = element_blank(), 
        axis.title.x = element_blank(), 
        legend.justification = "left", 
        legend.box.just = "left", 
        legend.margin = margin())
ggsave("relative-expression.png", width = 4, height = 3)
ggsave("relative-expression.pdf", width = 4, height = 3)

Coefficient of variation (CV) analysis


data <- steadyState(aplate_20210619_W303, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
#data <- tidyFlow(aplate_20210619_W303)

data <- subset(data, strain == "yWL161" & name %in% c("11L01.fcs", "11L07.fcs"))

#range(data$FL1.A)
#sd(data$FLratio)/mean(data$FLratio)
#range(data$FLratio)
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
[1]     722 2579491
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
[1] 1.821223
range(data$FLratio)
[1]   0.06052985 488.44186047
cv <- function(x) return(round(sd(x)/mean(x),2))

#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
# make a tidy, long dataset 

data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 
# need to also format CVs appropriately for annotating
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

#data
CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(parameter~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 0.1, y = 0.6) + 
  geom_text(data = subset(CVs, treatment == "Control EtOH"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 0.6) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot

Auxin prodcution in stationary phase

Read in annotated flowSet

flowSet <- read.flowSet(path = paste0("~/Google Drive/Shared drives/PlantSynBioLab/Auxin Biosensor Manuscript/Auxin Biosensor Data/FlowSets/", experiment_date, "_", experiment_name), phenoData = "annotation.txt")
write.flowSet(flowSet, "flowSets/auxin-biosynthesis")
flowSet <- read.flowSet(path = "flowSets/auxin-biosynthesis/", phenoData = "annotation.txt")

Summary Analysis

load("PSB_Accuri_W303.RData")
dat_sum <- summarizeFlow(flowSet, ploidy = "haploid", only = "yeast") # These gates might not work well for YPH499, but there was a lot of debris
[1] "Gating with haploid yeast gate..."
[1] "Summarizing all yeast events..."
dat_sum <- mutate(.data = dat_sum, across(starts_with("FL"), as.numeric)) %>%
  mutate(strain = str_remove(strain, " ratiometric sensor") %>% 
           str_replace(pattern = "in trans", 
                       replacement = "single-fusion") %>%
           str_replace(pattern = "in cis",
                       replacement = "dual-fusion"))
dat_sum$pred_auxin <- dat_sum$FL4.Amean/dat_sum$FL1.Amean 
dat_sum$pred_auxin_SE <- with(dat_sum, 
                              sqrt((FL4.Asd/100/FL4.Amean)^2 +
                              (FL1.Asd/100/FL1.Amean)^2)*pred_auxin)
dat_sum$pred_auxin_sd <- with(dat_sum, 
                              sqrt((FL4.Asd/FL4.Amean)^2 + 
                              (FL1.Asd/FL1.Amean)^2)*pred_auxin)
ggplot(data = dat_sum, 
       aes(x = fct_reorder(treat, pred_auxin), y = pred_auxin,
           color = factor(strain))) +
  geom_point() + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  facet_grid(.~strain, scales = "free_x") + coord_flip() +
  theme(legend.position = "bottom", 
        legend.direction = "vertical") + 
  labs(x = "", y = "Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_raw.pdf", width = 8, height = 3)

dat_sum <- dat_sum %>% group_by(strain) %>% 
  mutate(norm_pred_auxin = 
           pred_auxin / mean(pred_auxin
                             [which(.data$treat == 
                            "aerobic exponential phase")]))

ggplot(data = dat_sum, aes(x = fct_reorder(treat, norm_pred_auxin),
                           y = norm_pred_auxin,
                           color = factor(strain))) +
  geom_point(position = position_jitter(width = 0.2)) + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  coord_flip() + theme(legend.position = "top", 
                       legend.direction = "vertical") + 
  labs(x = "", 
       y = "Normalized Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_norm.pdf", width = 5, height = 5)

Full distributions

data <- steadyState(flowset = flowSet, ploidy = "haploid", only = "yeast")
[1] "Gating with haploid yeast gate..."
[1] "Converting events..."
hist(x = log(data$FL1.A, 10))

hist(x = log(data$FL4.A, 10))

data$pred_auxin <- data$FL4.A/data$FL1.A
data <- data %>% dplyr::filter(FL1.A > 1 & FL4.A > 1)
data$treat <- data$treat %>% str_remove("phase")
endog_auxin <- ggplot(
  data = subset(
    data,
    strain == "W303 ratiometric sensor AFB2 in cis" &
      pred_auxin < 2),
  aes(
    x = pred_auxin,
    y = fct_reorder(treat, .x = pred_auxin, 
                    .fun = median, .desc = TRUE) %>% 
      fct_relevel("aerobic exponential ", after = Inf),
    fill = factor(stat(quantile))
  )
) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = 4,
    color = "grey",
    alpha = 0.5
  ) +
  scale_fill_viridis_d(name = "Quartiles") +
  theme(legend.position = NULL) +
  #facet_wrap(.~treat) +
  xlim(c(-0.1, 1)) +
  labs(y = NULL, x = "Predicted relative auxin production (AU) \n (AFB2-mScarlet-I/Venus-IAA17)") + theme_ridges() + theme(legend.position = "none")
ggsave("20210626_auxin_production_quartiles.pdf", width = 5, height = 4)
endog_auxin

Biosensor sensitivity at stationary phase

plate_09282022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/Data/", pattern = "S-DRA*") 
annotation_09282022 <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/09282022_stationaryphase_annotation.csv')

aplate_09282022 <- annotateFlowSet(yourFlowSet = plate_09282022, annotation_df = annotation_09282022, mergeBy = 'name')
head(rownames(pData(aplate_09282022)))
head(pData(aplate_09282022))
write.flowSet(aplate_09282022, "flowSets/AFB2-dual-stationary")
aplate_09282022 <- read.flowSet(path = "flowSets/AFB2-dual-stationary/", phenoData = "annotation.txt")

dat_sumGr_09282022 <- summarizeFlow(aplate_09282022, gated = TRUE)
[1] "Summarizing all events..."
model.LL4_09282022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(dat_sumGr_09282022, set == "4"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

pm210stat <- expand.grid(treatment=exp(seq(log(1e-5), log(1e2), length=1000))) 
pm210stat <- cbind(pm210stat,predict(model.LL4_09282022, newdata=pm210stat, interval="confidence"))  #m2all = model of 210 data

plot210_stat <- ggplot(subset(dat_sumGr_09282022, set == "4"), 
                       aes(x = dose, y = YL1.Amean/BL1.Amean))  +
 geom_ribbon(data = pm210stat, 
             aes(x = treatment, y = Prediction, 
                 ymin = Lower, ymax=Upper), alpha = .2) +
  geom_line(data = pm210stat, aes(x = treatment, y = Prediction)) + 
  ylab("AFB2-mScarlet-I/Venus-IAA17") + 
  xlab("Exogenous Auxin (µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_d() + geom_point() + 
  theme_classic(base_size = 14) + 
  theme(legend.position = "none") 
plot210_stat

Combined figure

endog_auxin + plot210_stat + plot_annotation(tag_levels = "A")

ggsave("auxin-accumulation.pdf", width = 8, height = 4)
ggsave("auxin-accumulation.png", width = 8, height = 4)

Mutant library analysis

Here we aim to test auxin induced Venus-IAA17 degradation relative to the bicistronic mScarlet-I control with AFB2 expressed from the p1 plasmid in the OrthoRep continuous mutagenesis system.

Procedure

Temperature 30 degree celcius Shaking at 250rpm DO-URA-HIS, starting volume: 10 mL Overnight concentration: 30 events/uL 1PM started from 2 colonies

Initial reading ~10AM Auxin concentration: 100 uM (.1% DMSO) After 4th reading: B04.fcs

###Importing and annotating data

aplate1 <- read.flowSet(path = 'flowSets/OrthoRep', phenoData = "annotation.txt")
dat_sum <- summarizeFlow(aplate1, gated = TRUE)
[1] "Summarizing all events..."
data <- flowTime::tidyFlow(aplate1, gated = TRUE)
[1] "No further gating applied."
[1] "Converting events..."
data <- subset(data, FL4.A > 1)
range(data$FL1.A)
[1]        0 15867629
range(data$FL4.A)
[1]       6 7690137
data$ratio <- data$FL1.A / data$FL4.A
data <- subset(data, data$ratio < 10)

Make a kernel density plot overlapping mutant population with parent, DMSO and IAA that we can then stack a few timepoints with facets. For timepoints it looks like the 2nd, 7th and 12th would be a good demonstration of the timecourse.

wells <- dat_sum %>%
                 # Get well/file names of the 2nd, 7th and 12th readings of the 4 strains. 
                 dplyr::slice(1:4 + unlist(lapply((c(2, 7, 12) - 1)*4, rep, 4))) %>%
                 dplyr::pull("name")
data <- 
  dplyr::filter(data, name %in% wells)

data$approxtime <- signif(data$etime, digits = 1)
signif(unique(data$approxtime), 1)
[1]  30 200 600
data$approxtime <- as.factor(data$approxtime) %>% fct_recode("0 hour" = "30", "1 hour" = "200", "6 hour" = "600")
data$strain <- fct_recode(data$strain, "high fidelity" = "wild" , "error prone" = "mutant")
data <- 
  data %>% 
  mutate(Venus = FL1.A, 
         mScarlet = FL4.A, 
         ratio = Venus/mScarlet)

cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment, strain) %>% dplyr::summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

kernel_plot <- ggplot(data = data, 
       mapping = aes(x = ratio, 
                     color = treatment, linetype = strain)) + 
  geom_density() + coord_cartesian(x = c(0.5,2)) + 
  facet_grid(approxtime~.) + theme_test() + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) + 
  labs(x = "Venus-IAA17/mScarlet ratio", linetype = "polymerase")
kernel_plot
ggsave("orthorep_degradation.pdf", height = 4, width = 4)
ggsave("orthorep_degradation.png", height = 4, width = 4)

Gating Strategy

data <- aplate1[wells]

To gate out the high FSC-A debris we will use only the lower 99.5% of the data

g <- gate_quantile(fr = data[[1]], channel = "FSC.A", probs = 0.995)
autoplot(data[[1]], x = "FSC-A") + geom_gate(g)

Subset(data[[1]], !g)
flowFrame object 'A05.fcs'
with 9950 cells and 14 observables:
       name   desc     range  minRange  maxRange
$P1   FSC.A  FSC-A  16777216         0  16777216
$P2   SSC.A  SSC-A  16777216         0  16777216
$P3   FL1.A  FL1-A  16777216         0  16777216
$P4   FL2.A  FL2-A  16777216         0  16777216
$P5   FL3.A  FL3-A  16777216         0  16777216
...     ...    ...       ...       ...       ...
$P10  FL2.H  FL2-H  16777216         0  16777216
$P11  FL3.H  FL3-H  16777216         0  16777216
$P12  FL4.H  FL4-H  16777216         0  16777216
$P13  Width  Width  16777216         0  16777216
$P14   Time   Time  16777216         0  16777216
161 keywords are stored in the 'description' slot
autoplot(Subset(data[[3]], !g), x = "FSC-A", "SSC-A")

autoplot(data[[3]], "FSC-A", "SSC-A") + geom_gate(g)

Now we need to gate out only singlet cells

chnl <- c("FSC-A", "FSC-H")
singlets <- gate_singlet(x = Subset(data[[1]], !g), area = "FSC.A",height = "FSC.H", prediction_level = 0.999, maxit = 20)
autoplot(data[[1]], "FSC-A", "FSC-H") + geom_gate(singlets)

Now we can assess this singlets gate over for several frames

length(data)
[1] 12
autoplot(data, x = "FSC-A", y = "FSC-H") + geom_gate(singlets) + facet_wrap("name", ncol = 4) 


autoplot(Subset(data, !g) %>% Subset(singlets), x = "FL1-A") + facet_wrap("name", ncol = 4) 

autoplot(Subset(data, !g |singlets), x = "FSC-A", y = "SSC-A") + facet_wrap("name", ncol = 4)

This looks very consistent across the course of this experiment. We can summarize this gating across the whole experiment, but will not show this here.

#summary(filter(data, !g & singlets))
data <- Subset(data, !g & singlets)

Plot Fluorescence vs. Time

dat_sum <- summarizeFlow(data, gated = TRUE)
[1] "Summarizing all events..."
ggplot(data = dat_sum, aes(x = time, y = FL1.Amean/FL4.Amean, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)") + geom_text(aes(label = name))


ggplot(data = dat_sum, aes(x = time, y = conc, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)")

Define a gate containing ~95% of the untreated cells expressing the wildtype polymerase.

logt <- estimateLogicle(data[["E05.fcs"]], channels = c("FL4.A", "FL1.A"))
data <- transform(data[c("E05.fcs", "E06.fcs", "E07.fcs", "E08.fcs")], logt)
untreated <- gate_flowclust_2d(data[["E05.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "untreated")
treated <- gate_flowclust_2d(data[["E06.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "treated")



ggcyto(data, aes(x = "FL4.A", y = "FL1.A")) + 
  geom_point(color = "green4", alpha = 0.1, size = 0.5) + 
  ggthemes::theme_base() + 
  labs(x = "mScarlet-I (free FP)", y = "Venus-IAA17") +
  facet_grid(fct_rev(strain) %>% 
               fct_recode("wild-type" = "wild")~treatment) + 
  coord_cartesian(xlim = c(1.4,2.6), ylim = c(1.4,2.6)) + 
  geom_gate(untreated, colour = "magenta", linetype = 2) + 
  geom_gate(treated, colour = "magenta") +
  geom_stats(adjust = c(0.1, 0.05), size = 4.5, alpha = 0.5) 


ggsave("sorting-strategy.pdf", height = 4.5, width = 6)
ggsave("sorting-strategy.png", height = 4.5, width = 6)

Mutation frequency calculation

As an overestimate, these cells were cultured for ~12 generations over 24 hours. So this results in 212 cells for each initial.

AFB2 is 1725 bps, and the mutation rate of the error prone polymerase is calculated to be \(1 \times 10^{-5}\) substitutions per base. We will assume there is only 1 copy of the p1 plasmid per cell. We will also assume withing this coding sequence, for every 2.1 substitutions (sub) there is 1 nonsynonymous substitution (nsub), per the average across the codon table, not factoring in codon usage across TIR1.

\[ \begin{array}{c|c|c} 1 \times 10^{-5} \text{ sub} & 1725 \text{ bases} * 2 \text{ (bp)} & 1 \text{ nsub} \\ \hline \text{base} & \text{cell} & 2.1 \text{ sub} \end{array} = 0.02 \frac{\text{nsub}}{\text{cell}} \] So on the low end, ~0.0164286 percent of cells have a nonsynonymous substitution in TIR1. But because this substitution rate is really compounding over 12 generations, this estimate is quite low.

Based on this baseline rate per cell (or generation, cell duplication) \(r\), we can then compound this over \(t\) generations, to find the compounded rate \(r_c\). \[ r_c = (1 + r)^t-1 = (1+0.0164286)^{12}-1 = 0.2159686 \] And on the high end, which is a more accurate measure, ~22% of our population contains a nonsynonymous substitution in TIR1.

Session Info

sessionInfo()
R Under development (unstable) (2022-10-30 r83209)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] agricolae_1.3-5      ggthemes_4.2.4       patchwork_1.1.2      wesanderson_0.3.6    flowClust_3.37.0    
 [6] flowStats_4.11.0     ggcyto_1.27.4        flowWorkspace_4.11.0 ncdfFlow_2.45.0      BH_1.78.0-0         
[11] openCyto_2.11.1      gridExtra_2.3        drc_3.0-1            MASS_7.3-58.1        lubridate_1.9.2     
[16] forcats_1.0.0        purrr_1.0.1          readr_2.1.4          tibble_3.1.8         tidyverse_2.0.0     
[21] tidyr_1.3.0          dplyr_1.1.0          ggridges_0.5.4       stringr_1.5.0        ggplot2_3.4.1       
[26] flowTime_1.23.1      flowCore_2.11.0     

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3  rstudioapi_0.14     magrittr_2.0.3      TH.data_1.1-1       rainbow_3.7        
  [6] farver_2.1.1        ragg_1.2.5          zlibbioc_1.45.0     vctrs_0.5.2         RCurl_1.98-1.9     
 [11] htmltools_0.5.4     plotrix_3.8-2       haven_2.5.2         deSolve_1.34        hdrcde_3.4         
 [16] pracma_2.4.2        KernSmooth_2.23-20  plyr_1.8.8          sandwich_3.0-2      zoo_1.8-11         
 [21] mime_0.12           lifecycle_1.0.3     pkgconfig_2.0.3     Matrix_1.5-3        R6_2.5.1           
 [26] fastmap_1.1.0       shiny_1.7.4         digest_0.6.31       klaR_1.7-1          colorspace_2.0-3   
 [31] S4Vectors_0.37.3    textshaping_0.3.6   labeling_0.4.2      cytolib_2.11.0      fansi_1.0.3        
 [36] timechange_0.2.0    abind_1.4-5         compiler_4.3.0      withr_2.5.0         carData_3.0-5      
 [41] DBI_1.1.3           hexbin_1.28.2       highr_0.9           corpcor_1.6.10      gtools_3.9.4       
 [46] tools_4.3.0         rrcov_1.7-2         httpuv_1.6.9        glue_1.6.2          IDPmisc_1.1.20     
 [51] questionr_0.7.8     nlme_3.1-161        promises_1.2.0.1    grid_4.3.0          cluster_2.1.4      
 [56] generics_0.1.3      gtable_0.3.1        fda_6.0.5           labelled_2.10.0     tzdb_0.3.0         
 [61] data.table_1.14.6   hms_1.1.2           car_3.1-1           utf8_1.2.2          BiocGenerics_0.45.0
 [66] pillar_1.8.1        later_1.3.0         robustbase_0.95-0   splines_4.3.0       lattice_0.20-45    
 [71] AlgDesign_1.2.1     survival_3.4-0      deldir_1.0-6        ks_1.14.0           RProtoBufLib_2.11.0
 [76] tidyselect_1.2.0    RBGL_1.75.0         fds_1.8             miniUI_0.1.1.1      knitr_1.41         
 [81] stats4_4.3.0        xfun_0.35           Biobase_2.59.0      matrixStats_0.63.0  DEoptimR_1.0-11    
 [86] stringi_1.7.8       codetools_0.2-18    interp_1.1-3        Rgraphviz_2.43.0    graph_1.77.1       
 [91] cli_3.6.0           flowViz_1.63.0      systemfonts_1.0.4   xtable_1.8-4        munsell_0.5.0      
 [96] Rcpp_1.0.9          png_0.1-8           XML_3.99-0.13       parallel_4.3.0      ellipsis_0.3.2     
[101] mclust_6.0.0        latticeExtra_0.6-30 jpeg_0.1-10         bitops_1.0-7        viridisLite_0.4.1  
[106] mvtnorm_1.1-3       scales_1.2.1        crayon_1.5.2        pcaPP_2.0-3         combinat_0.0-8     
[111] rlang_1.0.6         multcomp_1.4-22     mnormt_2.1.1       

  1. Biological Systems Engineering, Virginia Tech, Blacksburg, VA , USA, co-first author↩︎

  2. Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA, co-first author↩︎

  3. Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA↩︎

  4. Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA↩︎

  5. Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA↩︎

  6. Biochemistry and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA↩︎

  7. Biological Systems Engineering, Fralin Life Sciences Institute, and Translational Plant Sciences Center, Virginia Tech, Blacksburg, VA, USA, ↩︎

---
title: "Supplmental reproducible analysis for: Genetically encoded, noise-tolerant,
  auxin biosensors in yeast facilitate metabolic engineering and directed evolution"
author:
- "Patarasuda Chaisupa^[Biological Systems Engineering, Virginia Tech, Blacksburg,
  VA , USA, co-first author]"
- "Mahbubur Rahman*^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA,
  USA, co-first author]"
- Sherry B. Hildreth^[Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA,
  USA]
- Saede Moseley^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA]
- Chauncey Gatling^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA,
  USA]
- Richard F. Helm^[Biochemistry and Fralin Life Sciences Institute, Virginia Tech,
  Blacksburg, VA, USA]
- R. Clay Wright^[Biological Systems Engineering, Fralin Life Sciences Institute,
  and Translational Plant Sciences Center, Virginia Tech, Blacksburg, VA, USA, wrightrc@vt.edu]
output:
  word_document: default
  html_document:
    df_print: paged
  pdf_document: default
  html_notebook: default
---

```{r setup, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, 
                      tidy = TRUE, message = FALSE, 
                      warning = FALSE)
library(flowCore)
library(flowTime)
library(ggplot2)
library(stringr)                        
library(ggridges)
library(dplyr)
library(tidyr)
library(tidyverse)
library(drc)
library(gridExtra)
library(openCyto)
library(ggcyto)
library(flowStats)
library(flowClust)
library(wesanderson)
library(patchwork)
library(ggthemes)
library(agricolae)
```

# Time-course response and ratiometric measurement of the single-fusion biosensors

## Time-course degradation

```{r, eval=FALSE}
# Read in flow sets from 20200611 and 20200614
flowSet <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Data/Mahbub/FlowSets/", pattern = "202006", phenoData = "annotation.txt")
flowSet <- flowSet[which(flowSet@phenoData@data$strain %in% c("T1T1", "A2A2"))]

write.flowSet(flowSet, "flowSets/single-time-course")

```


```{r}
flowSet <- read.flowSet(path =  "flowSets/single-time-course", phenoData = "annotation.txt")
# load gates for this strain/cytometer
load("PSB_Accuri_W303.RData")
data_sum <- summarizeFlow(flowset = flowSet, ploidy = "diploid", only = "singlets")
time0_14 <- data_sum %>% dplyr::filter(name == "21D10.fcs") %>% 
  pull(btime) 
time0_11 <- data_sum %>% dplyr::filter(name == "11G02.fcs") %>% 
  pull(btime) 
data_sum <- 
  data_sum %>% 
    mutate(time = case_when(
      folder == "20200611_AFB_epistasis" ~ 
        .$btime - time0_11, 
      folder == "20200614_AFB_epistasis" ~
        .$btime - time0_14
    ))
```

```{r}
shapes <- c("DMSO" = 1, "IAA" = 16)
lines <- c("DMSO" = 2, "IAA" = 1)

data_sum$ratio <- data_sum$FL1.Amean/data_sum$FL4.Amean
data_sum$Venus <- data_sum$FL1.Amean/10000
data_sum_long <- data_sum %>% 
  dplyr::select(time, treatment, yWL, folder, strain, ratio, Venus) %>%
  pivot_longer(cols = c(ratio, Venus), names_to = "parameter")
deg_plot <- ggplot(data = subset(data_sum_long, yWL == "166"), 
       aes(x = time, y = value, shape = treatment, 
           color = treatment, 
           group = interaction(treatment, folder))) + 
  geom_point() + labs(y = "intensity (AU)", x = "time post auxin addition (min)") +
  facet_wrap(~fct_rev(parameter), scales = "free") + 
  scale_shape_manual(values = shapes) + 
  geom_line(aes(linetype = treatment)) +
  scale_linetype_manual(values = lines) + 
  scale_color_viridis_d(option = "D", end = 0.75, direction = -1) + 
  theme_test()
deg_plot
```

```{r}
data <- flowTime::tidyFlow(flowSet, ploidy = "diploid", only = "singlets")
```

```{r}
# get late time points for one strain
last_reading <- 
  tail(unique(data[which(data$yWL == 166), "name"]),4) %>% head(2)
data <- dplyr::filter(data, yWL == "166" & name %in% last_reading)
# clean this up, cut off zeros
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
range(data$FLratio)
```

## Single-fusion CV plot 

```{r}
#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
# make a tidy, long dataset 
data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 

# need to also format CVs approriately for annotating
cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + coord_cartesian(x = c(0,2)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(fct_relevel(parameter, "Venus")~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "IAA"), 
             aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CVs, treatment == "DMSO"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 2) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot
```

```{r}
layout <- "
AAAAB
CCCCC
"
CV_plot  + guide_area() + deg_plot + plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(5, 2), design = layout) 
ggsave("ratio-deg.pdf", width = 5, height = 5)
ggsave("ratio-deg.png", width = 5, height = 5)
```

# Dose-response curves of dual-fusion AFB2 and TIR1 biosensors

## AFB2-based biosensor (yWL210 AFB2 dual-fusion, single ratiometric construct)

```{r, eval = F}
plate_all_210 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL210/All data/", pattern = "DRA-*") 
```

```{r, eval = F}

annotation <- createAnnotation(yourFlowSet = plate_all_210)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv')

```

```{r, eval = F}
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv'
  )

aplate_all_210 <-
  annotateFlowSet(yourFlowSet = plate_all_210,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_210)))
head(pData(aplate_all_210))
write.flowSet(aplate_all_210, 
              outdir = "flowSets/AFB2-dual-yWL210-dose-response")
```

```{r}
aplate_all_210 <-
  read.flowSet(path = "flowSets/AFB2-dual-yWL210-dose-response", 
               phenoData = "annotation.txt")

plate_all_210_sum <-
  summarizeFlow(aplate_all_210, channel = NA, gated = TRUE)
```

###Dose-response curve

#### Comparing log-logistic and Weibull models
(Figure 2 in Ritz (2009))
```{r}
fitdrc.m1 <-
  drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = LL.4())
fitdrc.m2 <-
 drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = W1.4())
#fitdrc.m3 <- 
# drm(YL1.Amean/BL1.Amean~dose, data=plate_all_210_sum, fct = W2.4())
#Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, :
#Convergence failed

summary(fitdrc.m1)
summary(fitdrc.m2)
#summary(fitdrc.m3)
```

The 4-parameter log-logistic model has a slightly lower residual standard error. 

```{r}
model.LL4_all_210 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = plate_all_210_sum,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)
summary(model.LL4_all_210)

```
### Individual replicate dose-response curves

```{r}
replicate1_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "1"),
    fct = LL.4()
  )
replicate2_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "2"),
    fct = LL.4()
  )
replicate3_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "3"),
    fct = LL.4()
  )
replicate4_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "4"),
    fct = LL.4()
  )
replicate5_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "5"),
    fct = LL.4()
  )
replicate6_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "6"),
    fct = LL.4()
  )
replicate7_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "7"),
    fct = LL.4()
  )

plot(
  replicate1_210,
  broken = TRUE,
  type = "all",
  col = "dark green",
  lty = 2
)
plot(
  replicate2_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark blue",
  lty = 2
)
plot(
  replicate3_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "yellow",
  lty = 2
)
plot(
  replicate4_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark grey",
  lty = 2
)
plot(
  replicate5_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark orange",
  lty = 2
)
plot(
  replicate6_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "brown",
  lty = 2
)
plot(
  replicate7_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark red",
  lty = 2
)
plot(
  model.LL4_all_210,
  broken = TRUE,
  add = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)

```

## TIR1-based biosensor (yWL209 TIR dual-fusion, single ratiometric construct)

```{r, eval = F}
plate_all_209 <-
  read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL209/", pattern = "DRA-*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_all_209)
write.csv(
  annotation,
  '/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
)
```

```{r, eval = F}
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
  )

aplate_all_209 <-
  annotateFlowSet(yourFlowSet = plate_all_209,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_209)))
head(pData(aplate_all_209))

write.flowSet(aplate_all_209, outdir = "flowSets/TIR1-dual-yWL209-dose-response")
```

```{r}
aplate_all_209 <-
  read.flowSet(path = "flowSets/TIR1-dual-yWL209-dose-response", 
               phenoData = "annotation.txt")

dat_sum_overlaydata_209 <-
  summarizeFlow(aplate_all_209, gated = TRUE)
```
###Dose-response curve
```{r}
model.LL4_rep1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep1_209,
  type = "all",
  col = "red",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep1_209,
  broken = TRUE,
  type = "confidence",
  col = "red",
  add = TRUE
)
print(summary(model.LL4_rep1_209))


model.LL4_rep2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep2_209,
  type = "all",
  col = "blue",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep2_209,
  broken = TRUE,
  type = "confidence",
  col = "blue",
  add = TRUE
)
print(summary(model.LL4_rep2_209))


model.LL4_rep3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep3_209,
  type = "all",
  col = "dark green",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep3_209,
  broken = TRUE,
  type = "confidence",
  col = "dark green",
  add = TRUE
)

print(summary(model.LL4_rep3_209))



model.LL4_all_209 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = dat_sum_overlaydata_209,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_209,
  type = "all",
  col = "black",
  lty = 1,
  lwd = 3
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  col = "black",
  add = TRUE
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)
print(summary(model.LL4_all_209))

replicate1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4()
  )
replicate2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4()
  )
replicate3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4()
  )


plot(
  replicate1_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "red",
  lty = 2
)
plot(
  replicate2_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "blue",
  lty = 2
)
plot(
  replicate3_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "dark green",
  lty = 2
)

```

## Combined Dose-response curveswith ggplot

```{r}
pm210 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm210 <-
  cbind(pm210,
        predict(model.LL4_all_210, newdata = pm210, interval = "confidence"))  

plot210 <-
  ggplot(plate_all_210_sum, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm210,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm210,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + ylab("mScarlet-I/Venus") + xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) +  
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) + 
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  ggtitle("AFB2")  
plot210


pm209 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm209 <-
  cbind(pm209,
        predict(model.LL4_all_209, newdata = pm209, interval = "confidence"))

plot209 <-
  ggplot(dat_sum_overlaydata_209, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm209,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm209,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + 
  ylab("mScarlet-I/Venus") + 
  xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) +
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  #ylim(0.095, 0.12) + 
  ggtitle("TIR1")
plot209

plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A")

ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.pdf",
  width = 6,
  height = 3
)
ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.png",
  width = 6,
  height = 3
)
```

# LCMS measurements of intracellular auxin

```{r, eval=FALSE}
plate_08052022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/Alldata/", pattern = "DRA*") 
```

```{r, eval=FALSE}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/08052022_alldata_annotation.csv')

aplate_08052022 <- annotateFlowSet(yourFlowSet = plate_08052022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_08052022)))
head(pData(aplate_08052022))
write.flowSet(aplate_08052022, "flowSets/AFB2-dual-DR-LCMS")
```
```{r}
aplate_08052022 <- read.flowSet(path = "flowSets/AFB2-dual-DR-LCMS",
                                phenoData = "annotation.txt")
plate_08052022_sum <- summarizeFlow(aplate_08052022, gated = TRUE)
model.LL4_08052022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(plate_08052022_sum, reading =="10"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))
summary(model.LL4_08052022)
pm_08052022 <- expand.grid(treatment=exp(seq(log(1e-3), log(1e2), length=1000))) 
pm_08052022 <- cbind(pm_08052022,predict(model.LL4_08052022, newdata=pm_08052022, interval="confidence"))  

plot_08052022 <- 
  ggplot(data=subset(plate_08052022_sum, reading =="10"), 
         aes(x = dose, y = YL1.Amean/BL1.Amean))  + 
  geom_ribbon(data = pm_08052022, 
              aes(x = treatment, y = Prediction, ymin = Lower,
                  ymax=Upper), alpha=0.4) + 
  geom_line(data = pm_08052022, 
            aes(x = treatment, y = Prediction)) + 
  xlab("Extracellular IAA (µM)") +
  ylab("AFB2-mScarlet-/Venus-IAA17") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) +  
  geom_point(color = "black") + 
  theme_classic(base_size = 12) +
  theme(legend.position = "none") 
plot_08052022

LCMSdata <- read.csv('08052022_yWL210_DRA-LCMS_data.csv')

model.LL4_08052022_LCMS<- drm(Observed_IAA_in_uM~Dose_IAA, data=LCMSdata, fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))
summary(model.LL4_08052022_LCMS)

plot(model.LL4_08052022_LCMS, type="all",col="black",lty=1, lwd=3, xlab = "Extracellular concentrations of IAA (uM)", ylab = "Observed IAA (uM)")

pm_08052022_LCMS <- expand.grid(treatment=exp(seq(log(1e-3), log(5e2), length=1000))) 
pm_08052022_LCMS <- cbind(pm_08052022_LCMS,
                          predict(model.LL4_08052022_LCMS,
                                  newdata=pm_08052022_LCMS,
                                  interval="confidence")) 
LCMS_intracellular <- 
ggplot(data = LCMSdata, 
       mapping = aes(x = Dose_IAA, 
                     y = Observed_IAA_in_uM)) + 
  geom_point() + 
  geom_ribbon(data = pm_08052022_LCMS, 
              aes(x = treatment, y = Prediction, ymin = Lower,
                  ymax=Upper), alpha=0.4) + 
  geom_line(data = pm_08052022_LCMS, 
            aes(x = treatment, y = Prediction)) +
  scale_x_log10(labels = 
                scales::label_number(drop0trailing = TRUE)) +
  theme_classic(base_size = 13) +
  theme(legend.position = "none") + 
  ylab("Intracellular IAA (µM)") + 
  xlab("Extracellular IAA (µM)")  

plot_08052022 + LCMS_intracellular + 
  plot_annotation(tag_levels = "A")
ggsave("LCMS-comparison.pdf", width=6, height = 3)
ggsave("LCMS-comparison.png", width=6, height = 3)
```

# Auxin-induced degradation time-course assay for the dual-fusion biosensors in different yeast strains

Two isolated colonies from each strain were selected at random and tested in auxin-induced protein degradation assay. IAA working solution was added to obtain the IAA concentration at 50 µM in each culture. The assay was carried out using ThermoFisher Attune NxT B/Y flow cytometer.

Strains:

-   yWL185 (TIR1 dual-fusion in W303)
-   yWL186 (AFB2 dual-fusion in W303)
-   yWL209 (TIR1 dual-fusion in YPH499)
-   yWL210 (AFB2 dual-fusion in YPH499)

```{r, eval = FALSE}
plate_03112022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/10readings/onlyPatstrains/", pattern = "TCA*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_03112022)
write.csv(annotation,'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
```

```{r, eval = F}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
aplate_03112022 <- annotateFlowSet(yourFlowSet = plate_03112022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_03112022)))
head(pData(aplate_03112022))

write.flowSet(aplate_03112022, outdir = "flowSets/dual-time-course")
```

```{r}
aplate_03112022 <-
  read.flowSet(path = "flowSets/dual-time-course/", phenoData = "annotation.txt")
plate_03112022_sum <- summarizeFlow(aplate_03112022, gated = TRUE)


plate_03112022_sum <-
  plate_03112022_sum %>% mutate(
    background_p = case_when(
      strain %in% c("yWL185", "yWL186") ~ "W303",
      strain %in% c("yWL209", "yWL210") ~ "YPH499"
    ),
    receptor_p = case_when(
      strain %in% c("yWL185", "yWL209") ~ "TIR1",
      strain %in% c("yWL186", "yWL210") ~ "AFB2"
    )
  )
```

```{r}
#The time auxin addition is equal to time zero
time0 <-
  "303112022-Pat-TCA03_Time-course assay_Auxin_yWL185-C1.fcs"
# or whatever well was being read when auxin was added

plate_03112022_sum$time <-
  plate_03112022_sum$btime - 
  plate_03112022_sum[[which(plate_03112022_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"

```

```{r}
#To normalize data

plate_03112022_sum <-
  plate_03112022_sum %>% mutate(ratio = BL1.Amean/YL1.Amean) %>%
  group_by(background_p, receptor_p) %>% 
  mutate(normalizedratio = ratio/mean(ratio))
```

```{r}
ratio <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = normalizedratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Normalized Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p) +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio
```

Without normalization

```{r}
ratio_raw <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = ratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p, scales = "free") +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio_raw
```

```{r}
ratio_raw / ratio + plot_annotation(tag_levels = "A") & theme(plot.background = element_blank())
ggsave("tc-strains.pdf", width = 5, height = 7)
ggsave("tc-strains.png", width = 5, height = 7)
```


```{r}
#normalize green and red fluorescent expression
plate_03112022_sum <-
  plate_03112022_sum %>% 
  mutate(green = BL1.Amean) %>%
  group_by(background_p, receptor_p) %>% 
  mutate(normalized_Greenexpression = BL1.Amean/mean(BL1.Amean)) %>%
  mutate(red = YL1.Amean) %>%
  mutate(normalized_Redexpression = YL1.Amean/mean(YL1.Amean))

normgreen <- qplot(x = time, y = normalized_Greenexpression, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized Venus expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#F1C40F", "#85929E"))+ theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 

normgreen

normred <-qplot(x = time, y = normalized_Redexpression, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#EC7063", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
normred 

green <- qplot(x = time, y = BL1.Amean, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized Venus expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#F1C40F", "#85929E"))+ theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
green

red <-qplot(x = time, y = YL1.Amean, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#EC7063", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
red 

conc <-qplot(x = time, y = conc, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("black", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
conc 
```

# CV plot

Using the above time course dataset we can compare coefficients of variation in the individual parameters and the ratio for each of the strains and biosensors at steady state.

## yWL185 (TIR1 dual-fusion, W303 yeast)

```{r}

data185 <- steadyState(aplate_03112022, gated = TRUE)
data185 <- subset(data185, strain == "yWL185" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL185-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL185-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data185 <- subset(data185, BL1.A >1 & YL1.A > 1)
data185$FLratio <- data185$BL1.A/data185$YL1.A
range(data185$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data185$Venus <- data185$BL1.A / median(data185$BL1.A)
data185$mScarlet <- data185$YL1.A / median(data185$YL1.A)
data185$FLratio <- data185$BL1.A / data185$YL1.A
data185$ratio <- data185$FLratio / median(data185$FLratio)



data_long185 <- data185 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV185 <-
  data185 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data185

plot185 <- ggplot(data = data_long185, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV185, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV185, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus185 <- ggplot(data185, aes(x=data185$Venus, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "Venus", y ="Density") + xlim(-0.1,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
#venus185

red185 <- ggplot(data185, aes(x=data185$mScarlet, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "mScarlet", y ="Density") + xlim(-0.1,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
#red185


ratio185<- ggplot(data185, aes(x=data185$ratio, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(-0.1,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV185, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.4, y = 2) + 
  geom_text(data = subset(CV185, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

#ratio185

plot185 <- grid.arrange(venus185, red185, ratio185, nrow=3, ncol=1)
plot185

```

## yWL186 (AFB2 dual-fusion, W303 yeast)

```{r}
data186 <- steadyState(aplate_03112022, gated = TRUE)
data186 <- subset(data186, strain == "yWL186" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL186-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL186-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data186 <- subset(data186, BL1.A >1 & YL1.A > 1)
data186$FLratio <- data186$BL1.A/data186$YL1.A
range(data186$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data186$Venus <- data186$BL1.A / median(data186$BL1.A)
data186$mScarlet <- data186$YL1.A / median(data186$YL1.A)
data186$FLratio <- data186$BL1.A / data186$YL1.A
data186$ratio <- data186$FLratio / median(data186$FLratio)



data_long186 <- data186 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV186 <-
  data186 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data186

plot186 <- ggplot(data = data_long186, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV186, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV186, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 



venus186 <- ggplot(data186, aes(x=data186$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus (normalized median)", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV186, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red186 <- ggplot(data186, aes(x=data186$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet (normalized median)", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") +  scale_fill_manual(values=c("#EC7063", "#999999")) + geom_text(data = subset(CV186, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio186<- ggplot(data186, aes(x=data186$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV186, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 2) + 
  geom_text(data = subset(CV186, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.3, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

plot186 <- grid.arrange(venus186, red186, ratio186, nrow=3, ncol=1)
plot186
```

## yWL209 (TIR1 dual-fusion, YPH499 yeast)

```{r}

data209 <- steadyState(aplate_03112022, gated = TRUE)
data209 <- subset(data209, strain == "yWL209" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL209-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL209-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data209 <- subset(data209, BL1.A >1 & YL1.A > 1)
data209$FLratio <- data209$BL1.A/data209$YL1.A
range(data209$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data209$Venus <- data209$BL1.A / median(data209$BL1.A)
data209$mScarlet <- data209$YL1.A / median(data209$YL1.A)
data209$FLratio <- data209$BL1.A / data209$YL1.A
data209$ratio <- data209$FLratio / median(data209$FLratio)



data_long209 <- data209 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV209 <-
  data209 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data209

plot209 <- ggplot(data = data_long209, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV209, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1) + 
  geom_text(data = subset(CV209, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus209 <- ggplot(data209, aes(x=data209$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red209 <- ggplot(data209, aes(x=data209$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio209<- ggplot(data209, aes(x=data209$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV209, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CV209, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))


plot209 <- grid.arrange(venus209, red209, ratio209, nrow=3, ncol=1)
plot209


```


## yWL210 (AFB2 dual-fusion, YPH499 yeast)

```{r}

data210 <- steadyState(aplate_03112022, gated = TRUE)
#data <- tidyFlow(aplate_20210619_W303)

data210 <- subset(data210, strain == "yWL210" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL210-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL210-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data210 <- subset(data210, BL1.A >1 & YL1.A > 1)
data210$FLratio <- data210$BL1.A/data210$YL1.A
range(data210$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data210$Venus <- data210$BL1.A / median(data210$BL1.A)
data210$mScarlet <- data210$YL1.A / median(data210$YL1.A)
data210$FLratio <- data210$BL1.A / data210$YL1.A
data210$ratio <- data210$FLratio / median(data210$FLratio)



data_long210 <- data210 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV210 <-
  data210 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data210

plot210 <- ggplot(data = data_long210, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV210, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV210, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) +
  facet_grid(parameter~.) 




venus210 <- ggplot(data210, aes(x=data210$Venus, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") +  geom_text(data = subset(CV210, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.5, y = 1) + 
  geom_text(data = subset(CV210, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.7, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red210 <- ggplot(data210, aes(x=data210$mScarlet, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.2) + 
  geom_text(data = subset(CV210, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.4, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio210<- ggplot(data210, aes(x=data210$ratio, group=treatment, color=treatment, fill = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV210, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 1.8) + 
  geom_text(data = subset(CV210, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 1.8) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
#ratio210

plot210 <- grid.arrange(venus210, red210, ratio210, nrow=3, ncol=1)
plot210

```

```{r}
dual_fusion_cv <- (venus210 + ggtitle("AFB2")+ 
                theme(plot.title = element_text(hjust = 0.5)) | 
                venus209 + ggtitle("TIR1") + 
                theme(plot.title = element_text(hjust = 0.5))) / 
               (red210 | red209) / 
             (ratio210 | ratio209) + 
  theme(title = element_text(hjust = 0))
dual_fusion_cv 
ggsave("dual_fusion_cv.pdf", width = 6.3, height = 6)
ggsave("dual_fusion_cv.png", width = 6.3, height = 6)
```

# Single-fusion vs dual-fusion comparison in W303 yeast strain

-   yWL161 (TIR1 single-fusion, W303 yeast)
-   yWL162 (AFB2 single-fusion, W303 yeast)
-   yWL185 (TIR1 dual-fusion, W303 yeast)
-   yWL186 (AFB2 dual-fusion, W303 yeast)

```{r, eval=FALSE}
plate_20210619_W303 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only/", pattern = "r*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_20210619_W303)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
```

```{r, eval=FALSE}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
aplate_20210619_W303 <- annotateFlowSet(yourFlowSet = plate_20210619_W303, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_W303)))
head(pData(aplate_20210619_W303))
write.flowSet(aplate_20210619_W303, outdir = "flowSets/design-relative-expression")
```

```{r}
aplate_20210619_W303 <- read.flowSet(path = "flowSets/design-relative-expression/", phenoData = "annotation.txt")
plate_20210619_W303_sum <- summarizeFlow(aplate_20210619_W303,  gated = TRUE)


#The time auxin addition is equal to time zero
time0 <- "4E01.fcs" 
# or whatever well was being read when auxin was added

plate_20210619_W303_sum$time <- plate_20210619_W303_sum$btime-plate_20210619_W303_sum[[which(plate_20210619_W303_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"

```

```{r}
plate_20210619_W303_sum <- plate_20210619_W303_sum %>%
  mutate(
    design = case_when(
      strain %in% c("yWL185","yWL186") ~ "dual-fusion",
    strain %in% c("yWL161","yWL162") ~ "single-fusion"), 
         fbox = case_when(
    strain %in% c("yWL161","yWL185") ~ "TIR1",
    strain %in% c("yWL162","yWL186") ~ "AFB2")) %>%
  mutate(design_construct = paste(fbox, design)) 

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(ratio = FL1.Amean/FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalizedratio = ratio/mean(ratio))

plate_20210619_W303_sum<-
  plate_20210619_W303_sum %>% mutate(green = FL1.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Greenexpression = FL1.Amean/mean(FL1.Amean))

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(red = FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Redexpression = FL4.Amean/mean(FL4.Amean))
```

```{r}
TIR1_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL161"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 


AFB2_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL162"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

TIR1_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL185"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + xlab("Time (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

AFB2_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL186"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

grid.arrange(AFB2_single, TIR1_single, TIR1_dual, AFB2_dual, nrow=2, ncol=4)
```

```{r}
W303ratio <- qplot(x = time, y = normalizedratio, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus") + scale_color_manual(values = c("#2E86C1", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

W303venus <- qplot(x = time, y = normalized_Greenexpression, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet") + scale_color_manual(values = c("#F1C40F", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

grid.arrange(W303venus, W303ratio, nrow=2, ncol=2)

```

```{r}
Venus_aov <- aov(FL1.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(Venus_aov)
(Venus_HSD.test <- 
    agricolae::HSD.test(Venus_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))
Venus_groups <- Venus_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(Venus_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))

```

```{r}
mScarlet_aov <- aov(FL4.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(mScarlet_aov)
(mScarlet_HSD.test <- 
    agricolae::HSD.test(mScarlet_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))
mScarlet_groups <- mScarlet_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(mScarlet_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))

```

```{r}
boxvenus <- 
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL1.Amean/1000,
         )) +
  geom_boxplot(
    aes(fill = fct_rev(treatment)), alpha = 0.7,
    outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
  geom_text(data = Venus_groups, 
            mapping = aes(y = 1.05*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#edc215")) +
  scale_color_manual(values = c("#8f8f8f", "#edc215")) +
  ylab("Venus") + 
  xlab("50 µM Auxin treatment") +  
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct) + 
  theme_classic() + labs(color = "") 


boxmScarlet <-
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL4.Amean/1000
         )) +
  geom_boxplot(aes(fill = fct_rev(treatment)), alpha = .7,
               outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
   geom_text(data = mScarlet_groups, 
            mapping = aes(y = 1.2*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#EC7063")) +
  scale_color_manual(values = c("#8f8f8f", "#EC7063")) +
  ylab("mScarlet-I") + 
  xlab("50 µM Auxin treatment") +   
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct, scales = "free_y") +
  theme_classic() + labs(color = "") + scale_y_log10()


guide_area() / boxvenus / boxmScarlet + 
  plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(.3, 1,1)) & 
  theme(legend.box = "horizontal", 
        legend.spacing = unit(0, "pt"), 
        legend.justification = "top", 
        legend.margin = margin(), 
        legend.box.spacing = unit(0, "pt"), 
        legend.background = element_blank(), 
        legend.box.background = element_blank(), 
        legend.title = element_blank(), 
        legend.key = element_blank())

ggsave("relative-expression-box.pdf", width = 6, height = 5)
ggsave("relative-expression-box.png", width = 6, height = 5)
```

```{r}


```


```{r}
plate_20210619_read3and12 <- read.flowSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only_read3and12/", alter.names = TRUE) 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_20210619_read3and12)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
```

```{r}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
aplate_20210619_read3and12<- annotateFlowSet(yourFlowSet = plate_20210619_read3and12, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_read3and12)))
head(pData(aplate_20210619_read3and12))

```

```{r}
W303_read3and12 <- summarizeFlow(aplate_20210619_read3and12,  gated = TRUE)
W303_read3and12 <- W303_read3and12 %>% 
  mutate(design = str_extract(design_construct, "(?<=\\s).*")) %>%
  mutate(design = str_extract(design_construct, ".*(?=\\s)")) 

venus3and12_unnorm <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL1.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), size = 2,
             position = position_jitterdodge(
               dodge.width = 0.7, 
               jitter.width = 0.5)) + 
  scale_color_manual(values=c("#F1C40F", "#5F6A6A")) +
  ylab("Venus") + theme_classic() +
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

mScarlet3and12_unnorm  <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL4.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), 
             size = 2, 
             position = position_jitterdodge(
               dodge.width = .7, 
               jitter.width =.5)) + 
  scale_color_manual(values=c("#E74C3C", "#5F6A6A")) +
  ylab("mScarlet-I") + theme_classic() + 
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

guide_area() / (venus3and12_unnorm | mScarlet3and12_unnorm) + 
  plot_layout(guides = "collect", 
              heights = c(1, 3))  +
  plot_annotation(tag_levels = "A") &
  theme(legend.position='top', legend.direction = "vertical",
        legend.title = element_blank(), 
        axis.title.x = element_blank(), 
        legend.justification = "left", 
        legend.box.just = "left", 
        legend.margin = margin())
ggsave("relative-expression.png", width = 4, height = 3)
ggsave("relative-expression.pdf", width = 4, height = 3)
```

Coefficient of variation (CV) analysis

```{r}

data <- steadyState(aplate_20210619_W303, gated = TRUE)
#data <- tidyFlow(aplate_20210619_W303)

data <- subset(data, strain == "yWL161" & name %in% c("11L01.fcs", "11L07.fcs"))

#range(data$FL1.A)
#sd(data$FLratio)/mean(data$FLratio)
#range(data$FLratio)
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
range(data$FLratio)
cv <- function(x) return(round(sd(x)/mean(x),2))

#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
# make a tidy, long dataset 

data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 
# need to also format CVs appropriately for annotating
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

#data
CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(parameter~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 0.1, y = 0.6) + 
  geom_text(data = subset(CVs, treatment == "Control EtOH"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 0.6) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot

```

# Auxin prodcution in stationary phase

Read in annotated flowSet
```{r, eval=FALSE}
flowSet <- read.flowSet(path = paste0("~/Google Drive/Shared drives/PlantSynBioLab/Auxin Biosensor Manuscript/Auxin Biosensor Data/FlowSets/", experiment_date, "_", experiment_name), phenoData = "annotation.txt")
write.flowSet(flowSet, "flowSets/auxin-biosynthesis")
```
```{r}
flowSet <- read.flowSet(path = "flowSets/auxin-biosynthesis/", phenoData = "annotation.txt")
```

## Summary Analysis

```{r}
load("PSB_Accuri_W303.RData")
dat_sum <- summarizeFlow(flowSet, ploidy = "haploid", only = "yeast") # These gates might not work well for YPH499, but there was a lot of debris
```

```{r}
dat_sum <- mutate(.data = dat_sum, across(starts_with("FL"), as.numeric)) %>%
  mutate(strain = str_remove(strain, " ratiometric sensor") %>% 
           str_replace(pattern = "in trans", 
                       replacement = "single-fusion") %>%
           str_replace(pattern = "in cis",
                       replacement = "dual-fusion"))
dat_sum$pred_auxin <- dat_sum$FL4.Amean/dat_sum$FL1.Amean 
dat_sum$pred_auxin_SE <- with(dat_sum, 
                              sqrt((FL4.Asd/100/FL4.Amean)^2 +
                              (FL1.Asd/100/FL1.Amean)^2)*pred_auxin)
dat_sum$pred_auxin_sd <- with(dat_sum, 
                              sqrt((FL4.Asd/FL4.Amean)^2 + 
                              (FL1.Asd/FL1.Amean)^2)*pred_auxin)
```

```{r}
ggplot(data = dat_sum, 
       aes(x = fct_reorder(treat, pred_auxin), y = pred_auxin,
           color = factor(strain))) +
  geom_point() + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  facet_grid(.~strain, scales = "free_x") + coord_flip() +
  theme(legend.position = "bottom", 
        legend.direction = "vertical") + 
  labs(x = "", y = "Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_raw.pdf", width = 8, height = 3)
```

```{r}
dat_sum <- dat_sum %>% group_by(strain) %>% 
  mutate(norm_pred_auxin = 
           pred_auxin / mean(pred_auxin
                             [which(.data$treat == 
                            "aerobic exponential phase")]))

ggplot(data = dat_sum, aes(x = fct_reorder(treat, norm_pred_auxin),
                           y = norm_pred_auxin,
                           color = factor(strain))) +
  geom_point(position = position_jitter(width = 0.2)) + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  coord_flip() + theme(legend.position = "top", 
                       legend.direction = "vertical") + 
  labs(x = "", 
       y = "Normalized Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_norm.pdf", width = 5, height = 5)

```

Full distributions

```{r}
data <- steadyState(flowset = flowSet, ploidy = "haploid", only = "yeast")

hist(x = log(data$FL1.A, 10))
hist(x = log(data$FL4.A, 10))

```

```{r}
data$pred_auxin <- data$FL4.A/data$FL1.A
data <- data %>% dplyr::filter(FL1.A > 1 & FL4.A > 1)
data$treat <- data$treat %>% str_remove("phase")
```


```{r}
endog_auxin <- ggplot(
  data = subset(
    data,
    strain == "W303 ratiometric sensor AFB2 in cis" &
      pred_auxin < 2),
  aes(
    x = pred_auxin,
    y = fct_reorder(treat, .x = pred_auxin, 
                    .fun = median, .desc = TRUE) %>% 
      fct_relevel("aerobic exponential ", after = Inf),
    fill = factor(stat(quantile))
  )
) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = 4,
    color = "grey",
    alpha = 0.5
  ) +
  scale_fill_viridis_d(name = "Quartiles") +
  theme(legend.position = NULL) +
  #facet_wrap(.~treat) +
  xlim(c(-0.1, 1)) +
  labs(y = NULL, x = "Predicted relative auxin production (AU) \n (AFB2-mScarlet-I/Venus-IAA17)") + theme_ridges() + theme(legend.position = "none")
ggsave("20210626_auxin_production_quartiles.pdf", width = 5, height = 4)
endog_auxin
```

## Biosensor sensitivity at stationary phase

```{r, eval=FALSE}
plate_09282022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/Data/", pattern = "S-DRA*") 
```

```{r, eval=FALSE}
annotation_09282022 <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/09282022_stationaryphase_annotation.csv')

aplate_09282022 <- annotateFlowSet(yourFlowSet = plate_09282022, annotation_df = annotation_09282022, mergeBy = 'name')
head(rownames(pData(aplate_09282022)))
head(pData(aplate_09282022))
write.flowSet(aplate_09282022, "flowSets/AFB2-dual-stationary")
```

```{r}
aplate_09282022 <- read.flowSet(path = "flowSets/AFB2-dual-stationary/", phenoData = "annotation.txt")

dat_sumGr_09282022 <- summarizeFlow(aplate_09282022, gated = TRUE)

model.LL4_09282022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(dat_sumGr_09282022, set == "4"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

pm210stat <- expand.grid(treatment=exp(seq(log(1e-5), log(1e2), length=1000))) 
pm210stat <- cbind(pm210stat,predict(model.LL4_09282022, newdata=pm210stat, interval="confidence"))  #m2all = model of 210 data

plot210_stat <- ggplot(subset(dat_sumGr_09282022, set == "4"), 
                       aes(x = dose, y = YL1.Amean/BL1.Amean))  +
 geom_ribbon(data = pm210stat, 
             aes(x = treatment, y = Prediction, 
                 ymin = Lower, ymax=Upper), alpha = .2) +
  geom_line(data = pm210stat, aes(x = treatment, y = Prediction)) + 
  ylab("AFB2-mScarlet-I/Venus-IAA17") + 
  xlab("Exogenous Auxin (µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_d() + geom_point() + 
  theme_classic(base_size = 14) + 
  theme(legend.position = "none") 
plot210_stat
```

## Combined figure

```{r, fig.width=8, fig.height=4}
endog_auxin + plot210_stat + plot_annotation(tag_levels = "A")
ggsave("auxin-accumulation.pdf", width = 8, height = 4)
ggsave("auxin-accumulation.png", width = 8, height = 4)
```


# Mutant library analysis

Here we aim to test auxin induced Venus-IAA17 degradation relative to the bicistronic mScarlet-I control with AFB2 expressed from the p1 plasmid in the OrthoRep continuous mutagenesis system. 

## Procedure

Temperature 30 degree celcius
Shaking at 250rpm 
DO-URA-HIS, starting volume: 10 mL
Overnight concentration: 30 events/uL
1PM started from 2 colonies

Initial reading ~10AM
Auxin concentration: 100 uM (.1% DMSO)
After 4th reading: B04.fcs

###Importing and annotating data

```{r}
aplate1 <- read.flowSet(path = 'flowSets/OrthoRep', phenoData = "annotation.txt")
dat_sum <- summarizeFlow(aplate1, gated = TRUE)
```

```{r tidyFlow}
data <- flowTime::tidyFlow(aplate1, gated = TRUE)
data <- subset(data, FL4.A > 1)
range(data$FL1.A)
range(data$FL4.A)
data$ratio <- data$FL1.A / data$FL4.A
data <- subset(data, data$ratio < 10)
```
Make a kernel density plot overlapping mutant population with parent, DMSO and IAA that we can then stack a few timepoints with facets. For timepoints it looks like the 2nd, 7th and 12th would be a good demonstration of the timecourse.

```{r}
wells <- dat_sum %>%
                 # Get well/file names of the 2nd, 7th and 12th readings of the 4 strains. 
                 dplyr::slice(1:4 + unlist(lapply((c(2, 7, 12) - 1)*4, rep, 4))) %>%
                 dplyr::pull("name")
data <- 
  dplyr::filter(data, name %in% wells)

data$approxtime <- signif(data$etime, digits = 1)
signif(unique(data$approxtime), 1)
data$approxtime <- as.factor(data$approxtime) %>% fct_recode("0 hour" = "30", "1 hour" = "200", "6 hour" = "600")
data$strain <- fct_recode(data$strain, "high fidelity" = "wild" , "error prone" = "mutant")
data <- 
  data %>% 
  mutate(Venus = FL1.A, 
         mScarlet = FL4.A, 
         ratio = Venus/mScarlet)

cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment, strain) %>% dplyr::summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

kernel_plot <- ggplot(data = data, 
       mapping = aes(x = ratio, 
                     color = treatment, linetype = strain)) + 
  geom_density() + coord_cartesian(x = c(0.5,2)) + 
  facet_grid(approxtime~.) + theme_test() + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) + 
  labs(x = "Venus-IAA17/mScarlet ratio", linetype = "polymerase")
kernel_plot
ggsave("orthorep_degradation.pdf", height = 4, width = 4)
ggsave("orthorep_degradation.png", height = 4, width = 4)

```

## Gating Strategy 

```{r}
data <- aplate1[wells]
```

To gate out the high FSC-A debris we will use only the lower 99.5% of the data 
```{r}
g <- gate_quantile(fr = data[[1]], channel = "FSC.A", probs = 0.995)
autoplot(data[[1]], x = "FSC-A") + geom_gate(g)
Subset(data[[1]], !g)
autoplot(Subset(data[[3]], !g), x = "FSC-A", "SSC-A")
```

```{r}
autoplot(data[[3]], "FSC-A", "SSC-A") + geom_gate(g)
```
Now we need to gate out only singlet cells
```{r}
chnl <- c("FSC-A", "FSC-H")
singlets <- gate_singlet(x = Subset(data[[1]], !g), area = "FSC.A",height = "FSC.H", prediction_level = 0.999, maxit = 20)
autoplot(data[[1]], "FSC-A", "FSC-H") + geom_gate(singlets)
```
Now we can assess this singlets gate over for several frames
```{r}
length(data)
autoplot(data, x = "FSC-A", y = "FSC-H") + geom_gate(singlets) + facet_wrap("name", ncol = 4) 

autoplot(Subset(data, !g) %>% Subset(singlets), x = "FL1-A") + facet_wrap("name", ncol = 4) 
```
```{r}
autoplot(Subset(data, !g |singlets), x = "FSC-A", y = "SSC-A") + facet_wrap("name", ncol = 4)
```

This looks very consistent across the course of this experiment. We can summarize this gating across the whole experiment, but will not show this here. 

```{r}
#summary(filter(data, !g & singlets))
data <- Subset(data, !g & singlets)
```


Plot Fluorescence vs. Time

```{r, fig.width = 4, fig.height = 4}
dat_sum <- summarizeFlow(data, gated = TRUE)

ggplot(data = dat_sum, aes(x = time, y = FL1.Amean/FL4.Amean, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)") + geom_text(aes(label = name))

ggplot(data = dat_sum, aes(x = time, y = conc, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)")
```
Define a gate containing ~95% of the untreated cells expressing the wildtype polymerase. 

```{r}
logt <- estimateLogicle(data[["E05.fcs"]], channels = c("FL4.A", "FL1.A"))
data <- transform(data[c("E05.fcs", "E06.fcs", "E07.fcs", "E08.fcs")], logt)
untreated <- gate_flowclust_2d(data[["E05.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "untreated")
treated <- gate_flowclust_2d(data[["E06.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "treated")



ggcyto(data, aes(x = "FL4.A", y = "FL1.A")) + 
  geom_point(color = "green4", alpha = 0.1, size = 0.5) + 
  ggthemes::theme_base() + 
  labs(x = "mScarlet-I (free FP)", y = "Venus-IAA17") +
  facet_grid(fct_rev(strain) %>% 
               fct_recode("wild-type" = "wild")~treatment) + 
  coord_cartesian(xlim = c(1.4,2.6), ylim = c(1.4,2.6)) + 
  geom_gate(untreated, colour = "magenta", linetype = 2) + 
  geom_gate(treated, colour = "magenta") +
  geom_stats(adjust = c(0.1, 0.05), size = 4.5, alpha = 0.5) 


ggsave("sorting-strategy.pdf", height = 4.5, width = 6)
ggsave("sorting-strategy.png", height = 4.5, width = 6)
```


## Mutation frequency calculation
 
As an overestimate, these cells were cultured for ~12 generations over 24 hours. So this results in 2^12^ cells for each initial. 

AFB2 is 1725 bps, and the mutation rate of the error prone polymerase is calculated to be $1 \times 10^{-5}$ substitutions per base. We will assume there is only 1 copy of the p1 plasmid per cell. We will also assume withing this coding sequence, for every 2.1 substitutions (sub) there is 1 nonsynonymous substitution (nsub), per the average across the codon table, not factoring in codon usage across TIR1.

$$
    \begin{array}{c|c|c}
     1 \times 10^{-5} \text{ sub}  & 
     1725 \text{ bases} * 2 \text{ (bp)} & 
     1 \text{ nsub} \\ \hline
      \text{base}                              & 
      \text{cell} & 
      2.1 \text{ sub}
    \end{array} = `r 10^-5 *2100*2/2.1` \frac{\text{nsub}}{\text{cell}} 
$$
So on the low end, ~`r (cell_n_rate <- 10^-5 *1725*2/2.1)` percent of cells have a nonsynonymous substitution in *TIR1*. But because this substitution rate is really compounding over 12 generations, this estimate is quite low. 

Based on this baseline rate per cell (or generation, cell duplication) $r$, we can then compound this over $t$ generations, to find the compounded rate $r_c$.
$$
r_c = (1 + r)^t-1 = (1+`r cell_n_rate`)^{12}-1 = 
  `r (1+cell_n_rate)^12 - 1`
$$
And on the high end, which is a more accurate measure, ~`r round(100*((1+cell_n_rate)^12 - 1), 0)`% of our population contains a nonsynonymous substitution in *TIR1*. 

# Session Info 
```{r}
sessionInfo()
```

